Secure data transmission in IoTs based on CLoG edge detection

Abstract

Cyber–physical Systems (CPS) have revolutionized the industry by utilizing the Internet of Things (IoT) for interconnecting various system components. IoT plays a significant role in the development of the smart system paradigm by building a link between physical components with the virtual world for improving smart services and quality of life. It significantly improves people’s activities, be it their personalized health care, living, or the way they monitor control, and organize the businesses. However, data streaming/communication over an open IoT environment creates several security issues that need to be taken care of, for transferring data securely. This paper presents an efficient information embedding solution for ensuring data security in a cyber–physicalnetwork. In this work, a new efficient edge detector called CLoG, (based on Canny and Laplacian of Gaussian detectors) has been developed and is used for the detection of edge areas in digital images. The secret information has been embedded in detected edges. The proposed detector finds finer edge details compared to state-of-art, making it possible to hide more information in a cover image. This, in turn, reduces the number of cover images required to transmit secret data and as such fulfills the requirements of resource-constrained platforms like IoT. Experimental results show that the proposed scheme is capable of providing high-quality stego-images with an average peak signal-to-noise ratio (PSNR) of 48.12 dB for a payload of 2 bits per pixel (bpp) payload. Further, the scheme has been analyzed for its efficacy in terms of histogram analysis, normalized cross-correlation (NCC), and other objective parameters. Besides, we show that the proposed scheme supports blind extraction, has less computational complexity, and is suitable for IoT based systems.